1. Field of the Invention
The present invention relates to the localization, in a digitized image, of a ring-shaped surface included between two substantially concentric geometric shapes of generally circular shape. More specifically, the present invention relates to the detecting of a ring extending between two circles of different radiuses, the circle having the smaller radius being strictly included in the circle of greater radius.
2. Discussion of the Related Art
An example of application of the present invention is the localization of the iris of an eye in a digitized image. Indeed, an eye may be characterized as being an assembly of substantially concentric elliptic geometric shapes: the eyebrows define a contour that is followed by eyelashes, which surround the eye ground, which includes a substantially circular iris containing a substantially circular pupil. In such an approximately concentric pattern, the position of the centers and radiuses of the two circular patterns of the pupil and of the iris is desired to be extracted, to extract from the image the ring forming the iris. For clarity, it is considered in the following description that the limits of the pupil and of the iris are perfect circles.
FIG. 1 illustrates, in a flowchart, an example of a known method for localizing an iris of an eye. FIGS. 2A to 2C illustrate the digital image which is the object of the method at different steps of the implementation thereof.
Such a method starts with a step 101 (ACQUIRING EYE IMAGE) in which a digital image of an eye is acquired. The eye is digitized so that the obtained image is full size (scale 1:1). Such an image may be obtained by any biometry terminal enabling capturing an eye image. For example, the acquisition is performed by a CCD digital camera of a 580×760-pixel image, in black and white by infrared illumination, the eye being placed at a few centimeters only of the camera.
Localizing an iris in such an image then consists of localizing the center and the radius of the pupil as well as the center and the radius of the iris. Indeed, although the pupil is strictly included in the iris, its is generally slightly off-centered with respect thereto.
A known method for determining the centers of the pupil and of the iris and their radiuses is based on the following observation. In infrared illumination, on the one hand, the iris contrasts on the white of the eye. On the other hand, the contour of the pupil contrasts with respect to the peripheral iris. This contrast translates on the digital image as very different levels of grey on either side of the limit between the iris and the cornea, on the one hand, and of the limit between the iris and the pupil, on the other hand. The gradient, in terms of levels of grey, of the points located on the iris or pupil contour is then very high. Generally, the contrast between the pupil and the iris is greater than the contrast between the iris and the cornea.
Localizing the iris consists of successively considering each point in the image as the possible center and of measuring the gradients of the points located on arcs of a circle centered on the considered possible center. The radiuses of these arcs of a circle vary within a range of possible radiuses of a pupil or of an iris at the considered digitization scale. For a 580×760-pixel image at scale 1:1, the pupil diameter is considered to range between 30 and 100 pixels, and the iris diameter is considered to range between 100 and 180 pixels. The center of the pupil or of the iris then is the point for which, in the radius range corresponding to the pupil, respectively, to the iris, the gradient variation is the most significant. The gradient variation calculations are performed by means of integro-differential operators.
To reduce the amount of calculation and the processing time, the integro-differential operators are applied, at step 102 (LOCATING IRIS) which follows acquisition 101, to successive grids of points representing possible centers. The successive grids have decreasing dimensions and pitches. Thus, in a first iteration 103 (i=0, s=s0), it is for example chosen to apply on the digitized image illustrated in FIG. 2A a first grid of dimensions close to those of the image and of a relatively large first pitch s0, for example, s0=25 pixels, that is, including a possible center every 25 pixels in both directions. Further, the centers of the iris and of the pupil being confounded or slightly off-centered, the center and contour of the sole pupil are first localized by applying the operators on arcs of a circle having diameters varying from 30 to 100 pixels.
At the next iteration i=1, the grid pitch is reduced to refine the center determination. This pitch reduction goes along with a reduction of the grid dimensions and a centering thereof in the region of highest gradient variations. As illustrated in FIG. 2B, it is considered, for example, that for second iteration i=1, pitch s1 is 10 pixels. If the number of points in the grid is constant, its size reduction is automatic with the pitch reduction.
Again, the integro-differential operators are applied for each point in the grid and the existence of a center or of a region in which, for several points, the gradient variation (levels of grey) is the strongest is detected.
For each iteration i and each corresponding pitch si, the process is thus repeated by applying in a block 104 (INTEGRO-DIFFERENTIAL OPERATOR) the integro-differential operators on grids of decreasing pitch si and of more and more reduced dimensions.
After each passing through block 104, a possible center and radius are obtained for the pupil at block 105 (CENTER & RADIUS), which are included in the grid of smaller pitch at the next iteration.
It is controlled at the following step 106 (i=V? si=SV?) whether a precision criterion is achieved. This criterion is defined by a number V of iterations or a pitch Sv of the grid for which the obtained center and radius are considered as being localized in a sufficiently accurate manner.
If not (N), a new grid of reduced dimensions and of smaller pitch, centered on the region for which the strongest gradient variation has been observed at the preceding iteration is redefined at step 107 (NEW GRID, i=i+1, si=si+1).
Generally, the process carries on until a maximum accuracy, that is, a grid having a pitch of one pixel, is reached, as illustrated in FIG. 2C. Such an accuracy enables exact determination of center CP and radius RP of the pupil.
Once precision test 106 is positive (Y), the iris is localized at the next step 108 (IRIS) by applying again a grid of possible centers to determine with the integro-differential operators the iris radius RI and, should the case arise, discriminate its center CI from center CP of the pupil with a maximum reliability. The operators are here applied on arcs of a circle having diameters ranging from 100 to 180. Since the circles are approximately concentric, it is not necessary to start again from a grid having the maximum pitch. A reduced number of iterations (or even a single iteration) may be used by centering on the center of the pupil a grid sized according to the maximum (physiologically) possible interval between the two centers. Such a center localization and radius determination of a second circle after localizing a first circle is described in U.S. Pat. No. 5,291,560, which is incorporated herein by reference.
The iris surface, that is, the ring-shaped surface between the pupillary circle of center CP and of radius RP and the iridian circle of center CI and of radius RI, is then determined with a maximum accuracy.
The surface thus obtained may be submitted to any appropriate digital processing. In the considered example of an iris, it generally is an iridian recognition method based on the matching 109 (MATCHING) of features extracted from the obtained surface, for example, in one of the ways described in above-mentioned U.S. Pat. No. 5,291,560 or in above-mentioned U.S. Pat. No. 5,572,596, or in international patent application WO 00/62239, all of which are incorporated herein by reference.
Generally, the method described in relation with FIGS. 1 and 2 enables localizing at least one circle by exact determination of its radius and of the position of its center.
A major disadvantage of such a method is the successive repetition of the same operations on grids of decreasing size and pitch. This imposes a great amount of calculation. Further, for a given point, for example, point A of FIG. 2A close to the searched centers, the calculations are repeated a great number of times. Accordingly, such a method has a slow implementation.
Further, the various gradient variation comparison operations and the corresponding calculations impose a relatively complex and bulky software structure. Further, at each iteration, for each possible center, the obtained data must be stored to be compared to those obtained for the other possible centers, to determine the region(s) of strongest gradient variation. This is necessary to recenter the next grid at step 107. Such a method thus requires using a significant memory surface.
In a completely different field, to recognize the presence of a face in a digitized image, a method for determining the presence of a cornea or of an iris has been provided to identify the presence of an eye and calculate a spacing between two eyes. This method consists of searching concentric geometric shapes by means of a Hough transform such as described in U.S. Pat. No. 3,069,654. which is incorporated herein by reference. Such a method consists of considering, for the involved geometric shape (here, a circle), each pixel of the digitized image as being at the periphery of a circle of a given perimeter (radius), and of approximately localizing the center of this circle. For each possible diameter of the searched circuit, an accumulator of same dimensions as the image is associated with the digitized image. Each accumulator memorizes, for a given radius, the number of times when a given point of the digitized image is determined as being the possible center of the searched circle. This is performed by incrementing, for each radius, an initially null weight assigned, in the accumulator linked to this radius, to the position of the possible center in the image.
The searched center and radius are then obtained by determining, for each considered radius, the point having the greatest weight and, for the different considered radiuses, that for which the possible center has the maximum weight with respect to the other possible centers determined by the first determination. Such a method is described, for example, in article “Detection of eye locations in unconstrained visual images” by R. Kothari and J. L. Mitchell, published in Proc. ICIP'96, III pp. 519-523 (1996), or in article “Eye spacing measurement for facial recognition” by M. Nixon, published in SPIE Proc., 575, pp. 279-285 (1985), both of which are incorporated herein by reference.
Such a method has the disadvantage of being also long to execute. Further, for each pixel in the image, it imposes using a bulky memory, since, for each possible radius, an array having a number of lines and columns equal to the number of lines and columns of the digitized image must be memorized.